Search: “llm”

SpeechMedAssist: Efficiently and Effectively Adapting Speech Language Models for Medical Consultation

arXiv:2601.04638v2 Announce Type: replace Abstract: Medical consultations are intrinsically speech-centric. However, most prior works focus on long-text-based interactions, which are cumbersome and pa

Content type

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

Research

SpidR-Adapt: A Universal Speech Representation Model for Few-Shot Adaptation

DGX agent

arXiv:2512.21204v2 Announce Type: replace Abstract: Human infants, with only a few hundred hours of speech exposure, acquire basic units of new languages, highlighting a striking efficiency gap compar

researcharxiv-cs-cl

21 Apr 2026

ST-pi: Structured SpatioTemporal VLA for Robotic Manipulation

arXiv:2604.17880v1 Announce Type: cross Abstract: Vision-language-action (VLA) models have achieved great success on general robotic tasks, but still face challenges in fine-grained spatiotemporal man

local-aiarxiv-cs-cv

Stable Language Guidance for Vision-Language-Action Models

arXiv:2601.04052v2 Announce Type: replace-cross Abstract: Vision-Language-Action (VLA) models have demonstrated impressive capabilities in generalized robotic control; however, they remain notoriously

Still Between Us? Evaluating and Improving Voice Assistant Robustness to Third-Party Interruptions

arXiv:2604.17358v1 Announce Type: new Abstract: While recent Spoken Language Models (SLMs) have been actively deployed in real-world scenarios, they lack the capability to discern Third-Party Interrup

applicationsarxiv-cs-cl

Structure-Aware Diversity Pursuit as an AI Safety Strategy against Homogenization

arXiv:2601.06116v2 Announce Type: replace-cross Abstract: Generative AI models reproduce the biases in the training data and can further amplify them through mode collapse. We refer to the resulting h

SVL: Goal-Conditioned Reinforcement Learning as Survival Learning

arXiv:2604.17551v1 Announce Type: new Abstract: Standard approaches to goal-conditioned reinforcement learning (GCRL) that rely on temporal-difference learning can be unstable and sample-inefficient d

Synthia: Scalable Grounded Persona Generation from Social Media Data

arXiv:2507.14922v2 Announce Type: replace Abstract: Persona-driven simulations are increasingly used in computational social science, yet their validity critically depends on the fidelity of the under

T-REN: Learning Text-Aligned Region Tokens Improves Dense Vision-Language Alignment and Scalability

arXiv:2604.18573v1 Announce Type: new Abstract: Despite recent progress, vision-language encoders struggle with two core limitations: (1) weak alignment between language and dense vision features, whi

local-aiarxiv-cs-cv

Table Question Answering in the Era of Large Language Models: A Comprehensive Survey of Tasks, Methods, and Evaluation

arXiv:2510.09671v2 Announce Type: replace Abstract: Table Question Answering (TQA) aims to answer natural language questions about tabular data, often accompanied by additional contexts such as text p

The Geometric Canary: Predicting Steerability and Detecting Drift via Representational Stability

arXiv:2604.17698v1 Announce Type: cross Abstract: Reliable deployment of language models requires two capabilities that appear distinct but share a common geometric foundation: predicting whether a mo

The Provenance Gap in Clinical AI: Evidence-Traceable Temporal Knowledge Graphs for Rare Disease Reasoning

arXiv:2604.17114v1 Announce Type: new Abstract: Frontier large language models generate clinically accurate outputs, but their citations are often fabricated. We term this the Provenance Gap. We teste

Tool Learning Needs Nothing More Than a Free 8B Language Model

arXiv:2604.17739v1 Announce Type: cross Abstract: Reinforcement learning (RL) has become a prevalent paradigm for training tool calling agents, which typically requires online interactive environments

ToxiFrench: Benchmarking and Enhancing Language Models via CoT Fine-Tuning for French Toxicity Detection

arXiv:2508.11281v3 Announce Type: replace Abstract: Detecting toxic content using language models is crucial yet challenging. While substantial progress has been made in English, toxicity detection in

UniComp: A Unified Evaluation of Large Language Model Compression via Pruning, Quantization and Distillation

arXiv:2602.09130v3 Announce Type: replace Abstract: Model compression is increasingly essential for deploying large language models (LLMs), yet existing comparative studies largely focus on pruning an

safetyarxiv-cs-lg

Vision-Braille: A Curriculum Learning Toolkit and Braille-Chinese Corpus for Braille Translation

arXiv:2407.06048v2 Announce Type: replace Abstract: We present Vision-Braille, the first publicly available end-to-end system for translating Chinese Braille extracted from images into written Chinese

applicationsarxiv-cs-cl

What makes an entity salient in discourse?

arXiv:2508.16464v2 Announce Type: replace Abstract: Entities in discourse vary in salience: main participants, objects and locations stay prominent, while others are quickly forgotten, raising questio

Who is the richest club in the championship? Detecting and Rewriting Underspecified Questions Improve QA Performance

arXiv:2602.11938v5 Announce Type: replace Abstract: Large language models (LLMs) perform well on well-posed questions, yet standard question-answering (QA) benchmarks remain far from solved. We argue

XOXO: Stealthy Cross-Origin Context Poisoning Attacks against AI Coding Assistants

model-releasesarxiv-cs-lg

arXiv:2503.14281v4 Announce Type: replace-cross Abstract: AI coding assistants are widely used for tasks like code generation. These tools now require large and complex contexts, automatically sourced

Tutorials

A Case Study on the Impact of Anonymization Along the RAG Pipeline

arXiv:2604.15958v1 Announce Type: cross Abstract: Despite the considerable promise of Retrieval-Augmented Generation (RAG), many real-world use cases may create privacy concerns, where the purported u

tutorialsarxiv-cs-cl

A Systematic Study of Training-Free Methods for Trustworthy Large Language Models

arXiv:2604.15789v1 Announce Type: new Abstract: As Large Language Models (LLMs) receive increasing attention and are being deployed across various domains, their potential risks, including generating

Adaptive multi-fidelity optimization with fast learning rates

arXiv:2604.16239v1 Announce Type: cross Abstract: In multi-fidelity optimization, biased approximations of varying costs of the target function are available. This paper studies the problem of optimiz

safetyarxiv-cs-lg

Aerial Multi-Functional RIS in Fluid Antennas-Aided Full-Duplex Networks: A Self-Optimized Hybrid Deep Reinforcement Learning Approach

arXiv:2604.14309v2 Announce Type: replace-cross Abstract: To address high data traffic demands of sixth-generation (6G) networks, this paper proposes a novel architecture that integrates autonomous ae

safetyarxiv-cs-ai

AHS: Adaptive Head Synthesis via Synthetic Data Augmentations

arXiv:2604.15857v1 Announce Type: new Abstract: Recent digital media advancements have created increasing demands for sophisticated portrait manipulation techniques, particularly head swapping, where

applicationsarxiv-cs-cv

ChemGraph-XANES: An Agentic Framework for XANES Simulation and Analysis

arXiv:2604.16205v1 Announce Type: cross Abstract: Computational X-ray absorption near-edge structure (XANES) is widely used to probe local coordination environments, oxidation states, and electronic s

CIG: Measuring Conversational Information Gain in Deliberative Dialogues with Semantic Memory Dynamics

arXiv:2604.15647v1 Announce Type: new Abstract: Measuring the quality of public deliberation requires evaluating not only civility or argument structure, but also the informational progress of a conve

CodeMMR: Bridging Natural Language, Code, and Image for Unified Retrieval

arXiv:2604.15663v1 Announce Type: cross Abstract: Code search, framed as information retrieval (IR), underpins modern software engineering and increasingly powers retrieval-augmented generation (RAG),

DataCenterGym: A Physics-Grounded Simulator for Multi-Objective Data Center Scheduling

arXiv:2604.15594v1 Announce Type: cross Abstract: Modern datacenters schedule heterogeneous workloads across geo-distributed sites with diverse compute capacities, electricity prices, and thermal cond

local-aiarxiv-cs-ai

Agents

Discover and Prove: An Open-source Agentic Framework for Hard Mode Automated Theorem Proving in Lean 4

arXiv:2604.15839v1 Announce Type: new Abstract: Most ATP benchmarks embed the final answer within the formal statement -- a convention we call 'Easy Mode' -- a design that simplifies the task relative

agentsarxiv-cs-ai

Enhancing Visual Representation with Textual Semantics: Textual Semantics-Powered Prototypes for Heterogeneous Federated Learning

arXiv:2503.13543v2 Announce Type: replace-cross Abstract: Federated Prototype Learning (FedPL) has emerged as an effective strategy for handling data heterogeneity in Federated Learning (FL). In FedPL

local-aiarxiv-cs-ai

Fragile Thoughts: How Large Language Models Handle Chain-of-Thought Perturbations

arXiv:2603.03332v3 Announce Type: replace-cross Abstract: Chain-of-Thought (CoT) prompting has emerged as a foundational technique for eliciting reasoning from Large Language Models (LLMs), yet the ro

GIST: Multimodal Knowledge Extraction and Spatial Grounding via Intelligent Semantic Topology

arXiv:2604.15495v1 Announce Type: new Abstract: Navigating complex, densely packed environments like retail stores, warehouses, and hospitals poses a significant spatial grounding challenge for humans

local-aiarxiv-cs-ai

HarmfulSkillBench: How Do Harmful Skills Weaponize Your Agents?

arXiv:2604.15415v1 Announce Type: cross Abstract: Large language models (LLMs) have evolved into autonomous agents that rely on open skill ecosystems (e.g., ClawHub and Skills.Rest), hosting numerous

Imperfectly Cooperative Human-AI Interactions: Comparing the Impacts of Human and AI Attributes in Simulated and User Studies

arXiv:2604.15607v1 Announce Type: cross Abstract: AI design characteristics and human personality traits each impact the quality and outcomes of human-AI interactions. However, their relative and join

researcharxiv-cs-ai

IPQA: A Benchmark for Core Intent Identification in Personalized Question Answering

arXiv:2510.23536v2 Announce Type: replace Abstract: Intent identification serves as the foundation for generating appropriate responses in personalized question answering (PQA). However, existing benc

Majority Voting for Code Generation

arXiv:2604.15618v1 Announce Type: new Abstract: We investigate Functional Majority Voting (FMV), a method based on functional consensus for code generation with Large Language Models, which identifies

Placing Puzzle Pieces Where They Matter: A Question Augmentation Framework for Reinforcement Learning

arXiv:2604.15830v1 Announce Type: new Abstract: Reinforcement learning has become a powerful approach for enhancing large language model reasoning, but faces a fundamental dilemma: training on easy pr

RAGognizer: Hallucination-Aware Fine-Tuning via Detection Head Integration

arXiv:2604.15945v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) is widely used to augment the input to Large Language Models (LLMs) with external information, such as recent or do

Reading Between the Lines: The One-Sided Conversation Problem

arXiv:2511.03056v2 Announce Type: replace-cross Abstract: Conversational AI is constrained in many real-world settings where only one side of a dialogue can be recorded, such as telemedicine, call cen

applicationsarxiv-cs-ai

RedBench: A Universal Dataset for Comprehensive Red Teaming of Large Language Models

arXiv:2601.03699v2 Announce Type: replace Abstract: As large language models (LLMs) become integral to safety-critical applications, ensuring their robustness against adversarial prompts is paramount.

Seed1.8 Model Card: Towards Generalized Real-World Agency

arXiv:2603.20633v3 Announce Type: replace Abstract: We present Seed1.8, a foundation model aimed at generalized real-world agency: going beyond single-turn prediction to multi-turn interaction, tool u

Self-Aligned Reward: Towards Effective and Efficient Reasoners

arXiv:2509.05489v2 Announce Type: replace Abstract: Reinforcement learning with verifiable rewards has significantly advanced reasoning in large language models (LLMs), but such signals remain coarse,

SocialGrid: A Benchmark for Planning and Social Reasoning in Embodied Multi-Agent Systems

arXiv:2604.16022v1 Announce Type: new Abstract: As Large Language Models (LLMs) transition from text processors to autonomous agents, evaluating their social reasoning in embodied multi-agent settings

Hardware

Taming Asynchronous CPU-GPU Coupling for Frequency-aware Latency Estimation on Mobile Edge

arXiv:2604.15357v1 Announce Type: cross Abstract: Precise estimation of model inference latency is crucial for time-critical mobile edge applications, enabling devices to calculate latency margins aga

hardwarearxiv-cs-ai

The Illusion of Equivalence: Systematic FP16 Divergence in KV-Cached Autoregressive Inference

arXiv:2604.15409v1 Announce Type: cross Abstract: KV caching is a ubiquitous optimization in autoregressive transformer inference, long presumed to be numerically equivalent to cache-free computation.

TPA: Next Token Probability Attribution for Detecting Hallucinations in RAG

arXiv:2512.07515v4 Announce Type: replace-cross Abstract: Detecting hallucinations in Retrieval-Augmented Generation remains a challenge. Prior approaches attribute hallucinations to a binary conflict

researcharxiv-cs-ai

TRIDENT: Enhancing Large Language Model Safety with Tri-Dimensional Diversified Red-Teaming Data Synthesis

arXiv:2505.24672v2 Announce Type: replace Abstract: Large Language Models (LLMs) excel in various natural language processing tasks but remain vulnerable to generating harmful content or being exploit

Truncated Kernel Stochastic Gradient Descent with General Losses and Spherical Radial Basis Functions

arXiv:2510.04237v5 Announce Type: replace Abstract: In this paper, we propose a novel kernel stochastic gradient descent (SGD) algorithm for large-scale supervised learning with general losses. Compar

safetyarxiv-cs-lg